At a glance
- Experience
- 2+ years experience
- Workplace
- Remote
- Location
- Singapore
About Workato
- Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes into a single platform so AI can reliably orchestrate business processes in production at enterprise scale. Built on more than a decade of running mission-critical processes for over half the Fortune 500 — including Nasdaq, Amazon, Cisco, Vodafone, Atlassian, and Lucid Motors — Workato turns over 14,000 enterprise systems AI needs to act on into one governed execution layer. For more information, visit workato.com.
About the role
- As an Analytics Engineer in the Product Management team, you will own the end-to-end delivery of robust, high-quality data products.
- You will be responsible for designing, developing, maintaining, and scaling mission-critical data models to provide reliable and accessible product usage data, proactively partnering with Data Engineers, Product Analysts, and business stakeholders.
- Your key mandate is to transform raw data into actionable insights that directly drive strategic product and business decisions, with a continuous focus on technical excellence and platform optimization.
Responsibilities
- Design, develop, and own scalable and maintainable data models using dbt (Data Build Tool), ensuring accurate, intuitive, and consistent data for all end users and stakeholders.
- Collaborate actively with Data Analysts and Business Stakeholders to translate complex reporting and analysis needs into production-ready, highly optimized dbt models.
- Enforce and evolve our internal dbt conventions and best practices, continuously optimizing the codebase for cleanliness, performance, and cost-efficiency.
- Own and enforce data quality and consistency by implementing robust testing, validation, and cleaning processes on mission-critical source tables.
- Implement and manage data monitoring and alerting solutions to ensure data flows and transformations are performing optimally and accurately, and proactively resolve data anomalies and pipeline failures.
- Create and maintain comprehensive data documentation and definitions (data dictionaries, process flows) to ensure data literacy, trust, and discoverability for stakeholders.
- Partner with data engineers, product analysts, GTM data teams, and other stakeholders to strategically align data insights with product improvements and business objectives.
- Communicate complex data architecture, patterns, and analytical conclusions effectively to both technical and non-technical audiences, driving consensus and action.
- Act as a data champion, evangelizing and guiding business users on the most efficient and reliable ways to leverage our data products, accelerating their time to insights.
- Lead the research and evaluation of new tools and technologies, like GenAI, for enhancing data engineering, orchestration, and analysis workflows.
Requirements
- 2+ years of experience in an Analytics Engineering or Data Warehousing role.
- Expert proficiency in SQL, including advanced techniques like window functions and proven ability in query performance optimization.
- Demonstrated expertise in dbt (Data Build Tool) for designing, developing, and maintaining complex data models, coupled with strong functional knowledge of a modern cloud data warehouse (e.g., Snowflake, BigQuery).
- Proven ability to apply data engineering best practices, including version control (Git/GitHub), modular coding, and automated testing, to maintain robust and reliable data pipelines.
- Strong understanding of data modeling principles (e.g., star/snowflake schemas, Slowly Changing Dimensions) and how to apply them to solve analytical business problems.
- Proficiency in Python or another scripting language is required.
- Experience with data orchestration tools (e.g., Airflow, Dagster) for building and managing data workflows.
- Resourceful, results-oriented, and autonomous, with a proven track record of owning the full lifecycle of analytical projects from ambiguous requirements to final delivery and business impact.
- Excellent verbal and written communication and stakeholder management skills, with the ability to translate complex data logic for non-technical audiences and effectively drive cross-functional alignment.
Skills
- SQL
- Dbt
- Snowflake
- BigQuery
- Git
- GitHub
- Python
- Airflow
- Dagster
